Required Libraries

library(dplyr)
library(knitr)
library(kableExtra)
library(stringr)
library(tidyr)
library(glue)
library(ggplot2)

Data Loading and Preprocessing

# Load the raw dataset
df <- read.csv("data/trauma-page-kidney-table.csv") %>% 
    select(-review.1, -review.2) 

# Identify excluded manuscripts for reporting
exdf <- df %>% filter(!str_starts(include, "y"))

# Process included manuscripts with comprehensive data cleaning
df <- df %>% filter(str_starts(include, "y")) %>%
    select(-pdf, -include) %>%
    mutate(
        reference = as.factor(reference),
        year = as.integer(year),
        age = as.numeric(age),
        gender = factor(gender),
        pmh = as.character(pmh),
        onset.y = as.numeric(onset.y),
        mechanism = factor(mechanism),
        ss = as.character(ss),
        hypertension = if_else(hypertension == "y", TRUE, FALSE),
        ua = factor(ua),
        grade = factor(grade, levels = sort(unique(grade), na.last = TRUE)),
        size.cm = as.numeric(size.cm),
        page.type = factor(page.type),
        laterality = factor(laterality),
        treatment = as.character(treatment),
        fu.status = if_else(str_trim(fu.status) == "s", TRUE, NA),
        fu.y = as.numeric(fu.y)
  )

# Clean urinalysis results for standardized reporting
df <- df %>%
  mutate(
    ua_clean = str_to_lower(ua),
    ua_result = case_when(
      ua_clean %in% c("hematuria", "trace blood") ~ "Positive",
      ua_clean == "negative" ~ "Negative",
      TRUE ~ NA_character_  # preserve NA when UA not done
    )
  )

# Standardize laterality reporting
df <- df %>%
  mutate(
    laterality = tolower(as.character(laterality)),
    laterality = gsub(",", "", laterality),
    laterality = str_trim(laterality),
    laterality = factor(laterality)
  )

Dataset Processing Summary:

Study Selection and Exclusions

# Process exclusion data
exdf <- exdf %>% 
  select(reference, include) %>%
  mutate(
    reference = as.factor(reference),
    include = as.factor(include)
  )

# Create exclusion summary table
exclusion_summary <- as.data.frame(table(exdf$include))
colnames(exclusion_summary) <- c("Reason for Exclusion", "Number of References")

exclusion_summary %>%
  kbl(caption = "Summary of Study Exclusions",
      align = "lc",
      booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"),
                full_width = FALSE,
                position = "center")
Summary of Study Exclusions
Reason for Exclusion Number of References
duplicate 1
not-available 6
path 2
patient level 2

Total excluded references: 11

Excluded references: Arai_1981, Barns_2022, Benendo-Kapuscinska_1999, El Madhoun_2009, Estrada_2019, Hoshiyama_2009, Oliveira_2002, Scott_1976, Shome_2002, Smyth_2012, van Ahlen_1987

Patient Demographics and Baseline Characteristics

Age Distribution Analysis

# Comprehensive age statistics using your original approach
describe_age <- function(x) {
  sprintf(
    "The median age at presentation was %.0f years (IQR: %.0f–%.0f, range: %.0f–%.0f). The mean age was %.1f ± %.1f years.",
    median(x, na.rm = TRUE),
    quantile(x, 0.25, na.rm = TRUE),
    quantile(x, 0.75, na.rm = TRUE),
    min(x, na.rm = TRUE),
    max(x, na.rm = TRUE),
    mean(x, na.rm = TRUE),
    sd(x, na.rm = TRUE)
  )
}

age_description <- describe_age(df$age)

# Create publication-quality age distribution plot
ggplot(df, aes(x = age)) +
  geom_histogram(aes(y = after_stat(density)), binwidth = 5, 
                 fill = "steelblue", color = "black", alpha = 0.7) +
  geom_density(color = "darkblue", linewidth = 1.2) +
  labs(
    title = "Age Distribution at Page Kidney Presentation",
    subtitle = paste0("n = ", sum(!is.na(df$age)), " patients"),
    x = "Age (years)",
    y = "Density"
  )

# Summary statistics table
age_stats <- data.frame(
  Statistic = c("n", "Median (IQR)", "Mean ± SD", "Range"),
  Value = c(
    sum(!is.na(df$age)),
    paste0(median(df$age, na.rm = TRUE), " (", 
           quantile(df$age, 0.25, na.rm = TRUE), "–", 
           quantile(df$age, 0.75, na.rm = TRUE), ")"),
    paste0(round(mean(df$age, na.rm = TRUE), 1), " ± ", 
           round(sd(df$age, na.rm = TRUE), 1)),
    paste0(min(df$age, na.rm = TRUE), "–", max(df$age, na.rm = TRUE))
  )
)

age_stats %>%
  kbl(caption = "Age Distribution Summary",
      align = "lc") %>%
  kable_styling(bootstrap_options = c("striped", "hover"),
                full_width = FALSE)
Age Distribution Summary
Statistic Value
n 24
Median (IQR) 24.5 (16.75–49.5)
Mean ± SD 34.2 ± 21.7
Range 9–89

Age Characteristics: The median age at presentation was 24 years (IQR: 17–50, range: 9–89). The mean age was 34.2 ± 21.7 years.

Gender Distribution

# Gender analysis maintaining your original approach
gender_counts <- table(df$gender)
total_patients <- nrow(df)

Gender Distribution: There was a marked male predominance in this cohort, with 23 males (95.8%) and 1 females (4.2%). This distribution is consistent with the literature on trauma-related renal injuries and reflects higher rates of trauma exposure in males.

Clinical Characteristics

Past Medical History Analysis

# Your original PMH processing approach with enhanced formatting
pmh_vec <- df$pmh

pmh_clean <- data.frame(id = seq_along(pmh_vec), pmh = pmh_vec) %>%
  mutate(pmh = str_to_lower(pmh)) %>%
  separate_rows(pmh, sep = ";\\s*") %>%
  mutate(pmh = str_trim(pmh)) %>%
  filter(pmh != "none" & pmh != "")

# Handle compound phrases that should yield 2+ categories
manual_split <- pmh_clean %>%
  filter(
    str_detect(pmh, "dm, polyneuropathy") |
    str_detect(pmh, "renal/pancreas") |
    str_detect(pmh, "sleep apnea.*htn")
  ) %>%
  mutate(category = case_when(
    str_detect(pmh, "dm, polyneuropathy") ~ list(c("Diabetes", "Neuropathy")),
    str_detect(pmh, "renal/pancreas") ~ list(c("Renal transplant", "Pancreas transplant")),
    str_detect(pmh, "sleep apnea.*htn") ~ list(c("Sleep apnea", "Hypertension"))
  )) %>%
  unnest_longer(category)

pmh_main <- pmh_clean %>%
  filter(!pmh %in% manual_split$pmh)

# categorization logic
pmh_main <- pmh_main %>%
  mutate(category = case_when(
    str_detect(pmh, "pancreas transplant|renal/pancreas|pancreas") ~ "Pancreas transplant",
    str_detect(pmh, "renal txp|renal transplant") ~ "Renal transplant",
    str_detect(pmh, "htn|hypertension|hctz|losartan|amlodipine") ~ "Hypertension",
    str_detect(pmh, "dm|diabetes") ~ "Diabetes",
    str_detect(pmh, "cad") ~ "Coronary artery disease",
    str_detect(pmh, "chronic renal|renal insufficiency|crf") ~ "Chronic renal insufficiency",
    str_detect(pmh, "glomerulonephritis") ~ "Glomerulonephritis",
    str_detect(pmh, "absent|congenital|atrophic") ~ "Congenital kidney anomaly",
    str_detect(pmh, "seizure") ~ "Seizure disorder",
    str_detect(pmh, "asthma") ~ "Asthma",
    str_detect(pmh, "sleep apnea|cpap") ~ "Sleep apnea",
    str_detect(pmh, "polyneuropathy|neuropathy") ~ "Neuropathy",
    str_detect(pmh, "fracture") ~ "Fracture",
    str_detect(pmh, "marijuana") ~ "Substance use",
    TRUE ~ "Other"
  ))

pmh_all <- bind_rows(pmh_main, manual_split)

# Get total number of patients
total_patients <- n_distinct(df$pmh)

# Create PMH summary table
pmh_summary <- pmh_all %>%
  count(category, name = "n") %>%
  arrange(desc(n)) %>%
  mutate(Percentage = round(100 * n / total_patients, 1))

# Professional visualization
ggplot(pmh_summary, aes(x = reorder(category, n), y = n)) +
  geom_col(fill = "#4C72B0") +
  coord_flip() +
  labs(
    title = "Distribution of Past Medical History",
    subtitle = paste0("Based on ", total_patients, " total patients"),
    x = "Medical Condition",
    y = "Number of Patients"
  )

# Summary table
pmh_summary %>%
  select(`Medical Condition` = category, n, `Percentage (%)` = Percentage) %>%
  kbl(caption = "Past Medical History Distribution",
      align = "lcc") %>%
  kable_styling(bootstrap_options = c("striped", "condensed"), 
                full_width = FALSE)
Past Medical History Distribution
Medical Condition n Percentage (%)
Renal transplant 4 36.4
Diabetes 3 27.3
Congenital kidney anomaly 2 18.2
Hypertension 2 18.2
Asthma 1 9.1
Chronic renal insufficiency 1 9.1
Coronary artery disease 1 9.1
Fracture 1 9.1
Neuropathy 1 9.1
Pancreas transplant 1 9.1
Seizure disorder 1 9.1
Sleep apnea 1 9.1
Substance use 1 9.1

Past Medical History Summary:

The most common comorbidities included hypertension (n = 2), diabetes (n = 3), and renal transplant (n = 4). Less frequent conditions included congenital kidney anomalies, chronic renal insufficiency, and sleep apnea.

Several patients had multiple comorbidities. Compound diagnoses such as diabetic neuropathy or sleep apnea–induced hypertension were classified under multiple categories to reflect their clinical relevance.

Time from Injury to Diagnosis

# onset analysis
onset_stats <- list(
  summary = summary(df$onset.y),
  mean = mean(df$onset.y, na.rm = TRUE),
  sd = sd(df$onset.y, na.rm = TRUE),
  quantiles = quantile(df$onset.y, probs = c(0.25, 0.5, 0.75), na.rm = TRUE)
)

# histogram
ggplot(df, aes(x = onset.y)) +
  geom_histogram(binwidth = 0.25, fill = "#FF8C00", color = "black") +
  labs(
    title = "Time from Injury to Diagnosis of Page Kidney",
    subtitle = paste0("n = ", sum(!is.na(df$onset.y)), " patients with reported onset time"),
    x = "Time to Diagnosis (years)",
    y = "Number of Patients"
  )

# log-scaled plot
ggplot(df, aes(x = onset.y)) +
  geom_histogram(binwidth = 0.25, fill = "steelblue", color = "black") +
  scale_x_log10() +
  labs(
    title = "Log-Scaled Time to Diagnosis of Page Kidney",
    subtitle = "Better visualization of the distribution spread",
    x = "Time to Diagnosis (log years)",
    y = "Number of Patients"
  )

# Summary statistics table
onset_summary_table <- data.frame(
  Statistic = c("n", "Median", "IQR", "Mean ± SD", "Range"),
  `Time (years)` = c(
    sum(!is.na(df$onset.y)),
    round(median(df$onset.y, na.rm = TRUE), 2),
    paste0(round(quantile(df$onset.y, 0.25, na.rm = TRUE), 2), "–", 
           round(quantile(df$onset.y, 0.75, na.rm = TRUE), 2)),
    paste0(round(mean(df$onset.y, na.rm = TRUE), 2), " ± ", 
           round(sd(df$onset.y, na.rm = TRUE), 2)),
    paste0(round(min(df$onset.y, na.rm = TRUE), 3), "–", 
           round(max(df$onset.y, na.rm = TRUE), 1))
  ),
  `Time (days)` = c(
    "",
    round(median(df$onset.y, na.rm = TRUE) * 365),
    paste0(round(quantile(df$onset.y, 0.25, na.rm = TRUE) * 365), "–", 
           round(quantile(df$onset.y, 0.75, na.rm = TRUE) * 365)),
    paste0(round(mean(df$onset.y, na.rm = TRUE) * 365), " ± ", 
           round(sd(df$onset.y, na.rm = TRUE) * 365)),
    paste0(round(min(df$onset.y, na.rm = TRUE) * 365), "–", 
           round(max(df$onset.y, na.rm = TRUE) * 365))
  )
)

onset_summary_table %>%
  kbl(caption = "Time from Injury to Page Kidney Diagnosis",
      align = "lcc") %>%
  kable_styling(bootstrap_options = c("striped", "hover"),
                full_width = FALSE)
Time from Injury to Page Kidney Diagnosis
Statistic Time..years. Time..days.
n 23
Median 0.04 15
IQR 0.01–0.67 5–243
Mean ± SD 1 ± 2.23 367 ± 813
Range 0–9 0–3285

Time from Injury to Diagnosis:

The time from injury to diagnosis of Page kidney varied substantially among patients. The median time to diagnosis was approximately 0.04 years (~15 days), with an interquartile range (IQR) of 0.01 to 0.67 years.

The mean time to diagnosis was 1 ± 2.23 years, and the range extended from 0 to 9 years.

This distribution reflects both acute presentations (diagnosed within days) and chronic or delayed diagnoses (up to several years), emphasizing the importance of clinical vigilance following trauma or renal interventions.

Mechanism of Injury

# mechanism analysis
total_patients <- nrow(df)
mechanism_summary <- as.data.frame(table(df$mechanism))
colnames(mechanism_summary) <- c("Mechanism", "Count")

mechanism_summary <- mechanism_summary %>%
  mutate(`Percentage (%)` = round(100 * Count / total_patients, 1))

mechanism_summary %>%
  kbl(caption = "Mechanism of Injury Leading to Page Kidney",
      align = "lcc") %>%
  kable_styling(bootstrap_options = c("striped", "hover"),
                full_width = FALSE)
Mechanism of Injury Leading to Page Kidney
Mechanism Count Percentage (%)
blunt 12 50
fall 6 25
mvc 6 25 
# Professional bar plot
ggplot(mechanism_summary, aes(x = reorder(Mechanism, -Count), y = Count)) +
  geom_col(fill = "#4C72B0") +
  labs(
    title = "Mechanism of Injury Leading to Page Kidney",
    x = "Mechanism of Injury",
    y = "Number of Cases"
  ) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Mechanism of Injury:

Among the patients included, the most common mechanisms of injury were blunt (12 cases, 50%), followed by fall (6 cases, 25%) and mvc (6 cases, 25%).

Clinical Presentation and Symptoms

# symptom processing
df <- df %>%
  mutate(
    ss_combined = ifelse(hypertension, 
                         ifelse(ss == "" | is.na(ss), "hypertension", paste(ss, "hypertension", sep = "; ")), 
                         ss)
  )

df <- df %>%
  mutate(
    ua_clean = str_to_lower(ua),
    ss_combined = ifelse(
      ua_clean == "hematuria" & !str_detect(str_to_lower(ss_combined), "hematuria"),
      ifelse(is.na(ss_combined) | ss_combined == "", "hematuria", paste(ss_combined, "hematuria", sep = "; ")),
      ss_combined
    )
  )

# Create ID and clean the combined symptom column
ss_clean <- data.frame(id = seq_along(df$ss_combined), ss = df$ss_combined) %>%
  mutate(ss = str_to_lower(ss)) %>%
  mutate(ss = str_replace_all(ss, ",", ";")) %>%
  separate_rows(ss, sep = ";\\s*") %>%
  mutate(ss = str_trim(ss)) %>%
  filter(ss != "") %>%
  distinct(id, ss)  

# symptom categorization
ss_clean <- ss_clean %>%
  mutate(symptom = case_when(
    str_detect(ss, "flank pain") ~ "Flank pain",
    str_detect(ss, "abdominal pain") ~ "Abdominal pain",
    str_detect(ss, "back pain") ~ "Back pain",
    str_detect(ss, "chest pain") ~ "Chest Pain",
    str_detect(ss, "nausea") ~ "Nausea",
    str_detect(ss, "vomiting") ~ "Vomiting",
    str_detect(ss, "hematemesis") ~ "Hematemesis",
    str_detect(ss, "headache") ~ "Headache",
    str_detect(ss, "syncope") ~ "Syncope",
    str_detect(ss, "hematuria") ~ "Hematuria",
    str_detect(ss, "palpitations|palpations") ~ "Palpitations",
    str_detect(ss, "oligou?ria") ~ "Oliguria",
    str_detect(ss, "anuria") ~ "Anuria",
    str_detect(ss, "fatigue") ~ "Fatigue",
    str_detect(ss, "distension") ~ "Abdominal distension",
    str_detect(ss, "ecchymosis") ~ "Ecchymosis",
    str_detect(ss, "hypertension") ~ "Hypertension",
    str_detect(ss, "aki") ~ "Acute kidney injury",
    str_detect(ss, "asymptomatic") ~ "Asymptomatic",
    TRUE ~ str_to_title(ss)
  ))

symptom_summary <- ss_clean %>%
  count(symptom, sort = TRUE) %>%
  mutate(Percentage = round(100 * n / n_distinct(ss_clean$id), 1))

symptom_summary %>%
  kbl(caption = "Presenting Symptoms and Signs",
      col.names = c("Symptom", "Count", "Percentage (%)"),
      align = "lcc") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"),
                full_width = FALSE)
Presenting Symptoms and Signs
Symptom Count Percentage (%)
Hypertension 13 92.9
Flank pain 8 57.1
Hematuria 7 50.0
Abdominal pain 4 28.6
Back pain 2 14.3
Headache 2 14.3
Abdominal distension 1 7.1
Asymptomatic 1 7.1
Chest Pain 1 7.1
Ecchymosis 1 7.1
Fatigue 1 7.1
Palpitations 1 7.1
Vomiting 1 7.1 
# Professional symptom plot
ggplot(symptom_summary, aes(x = reorder(symptom, n), y = n)) +
  geom_col(fill = "#D55E00") +
  geom_text(aes(label = paste0(Percentage, "%")), hjust = -0.1) +
  coord_flip() +
  labs(
    title = "Frequency of Presenting Symptoms and Signs",
    subtitle = "Clinical features at presentation",
    x = "Symptom/Sign",
    y = "Number of Patients"
  ) +
  ylim(0, max(symptom_summary$n) * 1.15)

Clinical Presentation:

Patients with Page kidney presented with a diverse range of clinical symptoms and signs. The most common presenting feature was Hypertension, reported in 13 patient(s) (92.9%). This was followed by Flank pain (8 patients, 57.1%) and Hematuria (7 patients, 50%).

Notably, 13 patients (92.9%) presented with hypertension as a clinical feature, emphasizing the hemodynamic impact of retroperitoneal compression. Additionally, 1 patient(s) were asymptomatic at diagnosis.

Diagnostic Findings

Urinalysis Results

ua_summary <- df %>%
  mutate(ua_result = case_when(
    is.na(ua) ~ "Missing",
    str_to_lower(ua) %in% c("hematuria") ~ "Positive",
    str_to_lower(ua) %in% c("negative") ~ "Negative",
    str_detect(str_to_lower(ua), "trace") ~ "Positive",
    TRUE ~ "Other"
  )) %>%
  count(ua_result, name = "n") %>%
  mutate(percent = round(100 * n / sum(n), 1))

ua_summary %>%
  kbl(caption = "Urinalysis Results Distribution",
      col.names = c("Urinalysis Result", "n", "Percentage (%)"),
      align = "lcc") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"),
                full_width = FALSE)
Urinalysis Results Distribution
Urinalysis Result n Percentage (%)
Missing 10 42
Negative 6 25
Positive 8 33

Urinalysis Findings: Urinalysis results were available in 14 of 24 cases. Among tested patients, hematuria (positive results) was found in 8 cases (33.3%), while 6 cases (25%) had negative urinalysis.

Injury Grade Assessment

grade_summary <- df %>%
  count(grade, name = "n", drop = FALSE) %>%
  mutate(
    Grade = ifelse(is.na(grade), "Missing", as.character(grade)),
    Percentage = round(100 * n / nrow(df), 1)
  ) %>%
  select(Grade, n, Percentage)

grade_summary %>%
  kbl(caption = "Distribution of AAST Kidney Injury Grades",
      align = "lcc") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                full_width = FALSE)
Distribution of AAST Kidney Injury Grades
Grade n Percentage
II 1 4.2
IV 1 4.2
V 1 4.2
Missing 21 87.5 
# Professional grade visualization
ggplot(grade_summary, aes(x = reorder(Grade, -n), y = n, fill = Grade)) +
  geom_col(width = 0.6, color = "black") +
  geom_text(aes(label = paste0(n, " (", Percentage, "%)")), 
            vjust = -0.5, size = 4) +
  labs(
    title = "AAST Kidney Injury Grade Distribution",
    subtitle = "Injury severity classification",
    x = "AAST Grade",
    y = "Number of Patients"
  ) +
  theme(legend.position = "none")

Injury Grading: Among the reported cases, AAST kidney injury grades were documented in a minority of patients. Grade V injuries were the most frequently reported (1, 4.2%), followed by grade IV (1, 4.2%) and grade II (1, 4.2%). However, the majority of cases (21, 87.5%) did not specify an AAST grade, limiting formal stratification by injury severity. This reflects the variability in reporting standards across case reports and underscores the need for consistent documentation of injury grading.

Page Kidney Imaging Characteristics

Size of Perinephric Fluid Collections

size_summary <- df %>%
  summarise(
    median = median(size.cm, na.rm = TRUE),
    iqr_low = quantile(size.cm, 0.25, na.rm = TRUE),
    iqr_high = quantile(size.cm, 0.75, na.rm = TRUE),
    min = min(size.cm, na.rm = TRUE),
    max = max(size.cm, na.rm = TRUE),
    mean = mean(size.cm, na.rm = TRUE),
    sd = sd(size.cm, na.rm = TRUE),
    missing = sum(is.na(size.cm)),
    total = n()
  )

# Size distribution histogram
ggplot(df %>% filter(!is.na(size.cm)), aes(x = size.cm)) +
  geom_histogram(bins = 12, fill = "#20B2AA", alpha = 0.8, color = "white") +
  geom_vline(aes(xintercept = median(size.cm, na.rm = TRUE)), 
             color = "red", linetype = "dashed", linewidth = 1) +
  labs(
    title = "Distribution of Fluid Collection Sizes",
    subtitle = paste0("n = ", sum(!is.na(df$size.cm)), " patients with reported measurements"),
    x = "Collection Size (cm)",
    y = "Number of Patients"
  )

# Size summary table
size_stats_table <- data.frame(
  Statistic = c("n", "Median (IQR)", "Mean ± SD", "Range", "Missing"),
  Value = c(
    sum(!is.na(df$size.cm)),
    paste0(size_summary$median, " (", size_summary$iqr_low, "–", size_summary$iqr_high, ")"),
    paste0(round(size_summary$mean, 1), " ± ", round(size_summary$sd, 1)),
    paste0(size_summary$min, "–", size_summary$max),
    paste0(size_summary$missing, " (", round(100 * size_summary$missing / size_summary$total, 1), "%)")
  )
)

size_stats_table %>%
  kbl(caption = "Perinephric Fluid Collection Size Statistics",
      align = "lc") %>%
  kable_styling(bootstrap_options = c("striped", "hover"),
                full_width = FALSE)
Perinephric Fluid Collection Size Statistics
Statistic Value
n 13
Median (IQR) 11 (7.4–12)
Mean ± SD 10.7 ± 4
Range 4–18
Missing 11 (45.8%)

Fluid Collection Size Analysis:

The size of the perinephric fluid collections on imaging at presentation varied widely. The median size was 11 cm (IQR: 7.4–12 cm), with a mean of 10.7 ± 4 cm. The smallest collection measured 4 cm, while the largest was 18 cm. Notably, size data was not reported in 11 out of 24 cases (45.8%).

Type of Fluid Collection

page_type_summary <- df %>%
  mutate(page_type_clean = ifelse(is.na(page.type), "Missing", as.character(page.type))) %>%
  count(page_type_clean, name = "n") %>%
  mutate(Percentage = round(100 * n / sum(n), 1))

page_type_summary %>%
  kbl(caption = "Type of Perinephric Fluid Collection",
      col.names = c("Collection Type", "n", "Percentage (%)"),
      align = "lcc") %>%
  kable_styling(bootstrap_options = c("striped", "hover"),
                full_width = FALSE)
Type of Perinephric Fluid Collection
Collection Type n Percentage (%)
Missing 1 4.2
hematoma 21 87.5
lymphocele 1 4.2
urinoma 1 4.2 
# Visualization
ggplot(page_type_summary, aes(x = reorder(page_type_clean, n), y = n)) +
  geom_col(fill = "#800000", alpha = 0.8) +
  geom_text(aes(label = paste0(n, " (", Percentage, "%)")), 
            hjust = -0.1, size = 4) +
  coord_flip() +
  labs(
    title = "Distribution of Fluid Collection Types",
    x = "Collection Type",
    y = "Number of Cases"
  ) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.15)))

Fluid Collection Types:

The type of perinephric fluid collection leading to Page kidney was most commonly a hematoma, accounting for 87.5% of cases (21 of 24 patients). Other etiologies included:

  • Urinoma: 1 case(s) (4.2%)
  • Lymphocele: 1 case(s) (4.2%)
  • Missing Data: 1 case(s) (4.2%)

These findings underscore that hematoma is the predominant cause of Page kidney across reported cases.

Laterality Distribution

laterality_table <- df %>%
  count(laterality) %>%
  mutate(Percentage = round(100 * n / sum(n), 1))

laterality_table %>%
  kbl(caption = "Distribution of Laterality in Page Kidney Cases",
      align = "lcc") %>%
  kable_styling(bootstrap_options = c("striped", "hover"),
                full_width = FALSE)
Distribution of Laterality in Page Kidney Cases
laterality n Percentage
allograft left 2 8.3
allograft right 2 8.3
bilateral 2 8.3
left 10 41.7
right 8 33.3 
# Laterality visualization
ggplot(laterality_table, aes(x = reorder(laterality, n), y = n)) +
  geom_col(fill = "#2F4F4F", alpha = 0.8) +
  geom_text(aes(label = paste0(n, " (", Percentage, "%)")), 
            hjust = -0.1, size = 4) +
  coord_flip() +
  labs(
    title = "Laterality Distribution of Page Kidney",
    x = "Kidney Location",
    y = "Number of Cases"
  ) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.15)))

Laterality Patterns:

In this cohort, Page kidney most commonly involved the left kidney (10 cases, 41.7%), followed by the right kidney (8 cases, 33.3%). Bilateral involvement was seen in 2 cases. Notably, allograft-related Page kidney occurred in a total of 4 cases, split between left and right allografts.

Anatomical Considerations: The laterality distribution may reflect important anatomical differences between the kidneys. The left kidney sits slightly higher than the right and has a longer renal vein, while the right kidney’s proximity to the liver may influence vulnerability patterns in trauma. In transplant cases, allograft anatomy and surgical positioning factors may contribute to the observed distribution patterns.

Treatment Modalities and Management

treatment_categories <- data.frame(
  Category = c("Conservative Management", "Percutaneous/IR Drainage", "Urologic Interventions", 
               "Surgical Decompression", "Nephrectomy", "Embolization/Vascular"),
  Definition = c("Medical management only, no procedural interventions",
                "US- or CT-guided drainage, non-surgical",
                "Involving stents or urinary diversion",
                "Operative evacuation of hematoma without nephrectomy",
                "Total or partial nephrectomy (open/lap)",
                "IR embolization or stenting of vessels"),
  Examples = c("conservative, lisinopril, clonidine, captopril, propranolol",
              "US drainage, CT perc drain, IR drainage, tPA, fibrinolysis",
              "ureteral stent, ureteral stet, labetalol + stent",
              "surgical evacuation, laparoscopic fenestration, decortication, renorrhaphy",
              "lap nephrectomy, total nephrectomy, surgical evacuation + nephrectomy",
              "IR coil embolization, splenic vein stent")
)

treatment_categories %>%
  kbl(caption = "Treatment Category Definitions and Examples") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"),
                full_width = TRUE) %>%
  column_spec(2, width = "35%") %>%
  column_spec(3, width = "35%")
Treatment Category Definitions and Examples
Category Definition Examples
Conservative Management Medical management only, no procedural interventions conservative, lisinopril, clonidine, captopril, propranolol
Percutaneous/IR Drainage US- or CT-guided drainage, non-surgical US drainage, CT perc drain, IR drainage, tPA, fibrinolysis
Urologic Interventions Involving stents or urinary diversion ureteral stent, ureteral stet, labetalol + stent
Surgical Decompression Operative evacuation of hematoma without nephrectomy surgical evacuation, laparoscopic fenestration, decortication, renorrhaphy
Nephrectomy Total or partial nephrectomy (open/lap) lap nephrectomy, total nephrectomy, surgical evacuation + nephrectomy
Embolization/Vascular IR embolization or stenting of vessels IR coil embolization, splenic vein stent 
df <- df %>%
  mutate(treatment_lower = tolower(treatment))

df_flags <- df %>%
  mutate(
    Conservative = str_detect(treatment_lower, "conservative|lisinopril|clonidine|captopril|amlodipine|propran|methyldopa|hctz|hydralazine|felodipine|labetolol|nitroprusside") &
                   !str_detect(treatment_lower, "drain|nephrectomy|evacuation|decortication|fenestration|stent|embol|coil|surgery|lap|ureter"),

    Perc_IR = str_detect(treatment_lower, "drain|fibrinolysis|fine needle|tp[a]?") &
              !str_detect(treatment_lower, "surgery|nephrectomy"),

    Urologic = str_detect(treatment_lower, "ureteral stent|stet"),

    Surgical_Decompression = str_detect(treatment_lower, "surgical evacuation|decortication|renorrhaphy|laparotomy|debridement|fenestration") &
               !str_detect(treatment_lower, "nephrectomy"),

    Nephrectomy = str_detect(treatment_lower, "nephrectomy"),

    Vascular = str_detect(treatment_lower, 
                          "embolization|coil|splenic vein stent")
  )

# Pivot longer to get counts
treatment_summary <- df_flags %>%
  select(reference, starts_with(c("Conservative", "Perc_IR", "Urologic", 
                                  "Surgical_Decompression", "Nephrectomy", 
                                  "Vascular"))) %>%
  pivot_longer(cols = -reference, names_to = "Category", 
               values_to = "Present") %>%
  filter(Present) %>%
  count(Category, name = "n") %>%
  mutate(Percentage = round(100 * n / nrow(df), 1))

treatment_summary %>%
  kbl(caption = "Distribution of Treatment Modalities",
      col.names = c("Treatment Category", "n", "Percentage (%)"),
      align = "lcc") %>%
  kable_styling(bootstrap_options = c("striped", "hover"),
                full_width = FALSE)
Distribution of Treatment Modalities
Treatment Category n Percentage (%)
Conservative 3 12.5
Nephrectomy 5 20.8
Perc_IR 6 25.0
Surgical_Decompression 9 37.5
Urologic 2 8.3
Vascular 2 8.3 
# Treatment distribution plot
ggplot(treatment_summary, aes(x = reorder(Category, -n), y = n)) +
  geom_bar(stat = "identity", fill = "#DAA520") +
  geom_text(aes(label = paste0(n, " (", Percentage, "%)")), 
            vjust = -0.5, size = 4) +
  labs(
    title = "Distribution of Treatment Modalities in Page Kidney",
    x = "Treatment Category",
    y = "Number of Patients"
  ) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.15)))

Treatment Approaches:

Treatment modalities varied considerably across the cohort. The most frequently employed approach was Surgical_Decompression (9 cases, 37.5%), followed by Perc_IR (6 cases, 25%).

The diversity in treatment approaches reflects the evolving management strategies for Page kidney, with considerations including severity of presentation, patient comorbidities, institutional expertise, and response to initial conservative measures.

Clinical Outcomes and Follow-up

df$fu.y <- as.numeric(df$fu.y)

# Summary stats
followup_summary <- df %>%
  summarize(
    `Number with Reported Follow-Up` = sum(!is.na(fu.y)),
    `Mean (years)` = round(mean(fu.y, na.rm = TRUE), 2),
    `Median (years)` = round(median(fu.y, na.rm = TRUE), 2),
    `Min (years)` = round(min(fu.y, na.rm = TRUE), 2),
    `Max (years)` = round(max(fu.y, na.rm = TRUE), 2)
  )

followup_summary %>%
  kbl(caption = "Follow-up Duration Summary",
      align = "lc") %>%
  kable_styling(bootstrap_options = c("striped", "hover"),
                full_width = FALSE)
Follow-up Duration Summary
Number with Reported Follow-Up Mean (years) Median (years) Min (years) Max (years)
20 1.1 0.38 0.01 9 
ggplot(df %>% filter(!is.na(fu.y)), aes(x = fu.y)) +
  geom_histogram(binwidth = 1, fill = "#006400", color = "black") +
  labs(
    title = "Distribution of Follow-up Duration",
    subtitle = paste0("n = ", sum(!is.na(df$fu.y)), " patients with reported follow-up"),
    x = "Years of Follow-Up",
    y = "Number of Patients"
  )

# Survival/outcome summary
survival_summary <- data.frame(
  Outcome = c("Total cases in study", "Cases with follow-up data", 
              "Documented survival", "Reported mortality"),
  Count = c(nrow(df), sum(!is.na(df$fu.y)), 
            sum(df$fu.status == TRUE, na.rm = TRUE), 
            sum(df$fu.status == FALSE, na.rm = TRUE)),
  Percentage = c(100, round(100 * sum(!is.na(df$fu.y)) / nrow(df), 1),
                round(100 * sum(df$fu.status == TRUE, 
                                na.rm = TRUE) / sum(!is.na(df$fu.y)), 1),
                0)
)

survival_summary %>%
  kbl(caption = "Clinical Outcomes Summary",
      align = "lcc") %>%
  kable_styling(bootstrap_options = c("striped", "hover"),
                full_width = FALSE)
Clinical Outcomes Summary
Outcome Count Percentage
Total cases in study 24 100
Cases with follow-up data 20 83
Documented survival 23 115
Reported mortality 0 0

Follow-up and Clinical Outcomes:

Among the reported cases, no mortality was observed, and all patients survived through their respective follow-up durations. Follow-up data were available for 20 out of 24 cases (83.3%).

The average duration of follow-up was 1.1 years (median: 0.4 years), ranging from 0 to 9 years.

While the absence of mortality is encouraging, the lack of standardized long-term outcome reporting (e.g., blood pressure control, renal function) limits broader conclusions. Nevertheless, these data suggest that with timely recognition and appropriate intervention, short- to mid-term prognosis for Page kidney is excellent.

Data Export and Workspace Management

pmh_cleaned <- pmh_all %>%
  group_by(id) %>%
  summarise(
    pmh = paste(unique(category), collapse = "; "),
    .groups = "drop"
  )

ss_cleaned <- ss_clean %>%
  group_by(id) %>%
  summarise(
    ss = paste(unique(symptom), collapse = "; "),
    .groups = "drop"
  )

# Select only the treatment flag columns
treatment_cols <- c("Conservative", "Perc_IR", "Urologic", 
                    "Surgical_Decompression", "Nephrectomy", "Vascular")

# Create a treatment summary
treatment_cleaned <- df_flags %>%
  mutate(id = row_number()) %>%
  pivot_longer(
    cols = all_of(treatment_cols),
    names_to = "category",
    values_to = "present"
  ) %>%
  filter(present) %>%
  group_by(id) %>%
  summarise(
    treatment_summary = paste(category, collapse = "; "),
    .groups = "drop"
  )

df <- df %>%
  mutate(id = row_number()) %>%
  left_join(pmh_cleaned, by = "id") %>%
  left_join(ss_cleaned, by = "id") %>%
  left_join(treatment_cleaned, by = "id")

# Making final table
df_final <- df %>%
  select(
    reference, year, age, gender,
    pmh = pmh.y,
    onset.y, mechanism,
    ss = ss.y,
    hypertension,
    ua = ua_result,
    grade, size.cm, page.type, laterality,
    treatment = treatment_summary,
    fu.status, fu.y
  )

# Export cleaned data
write.csv(df_final, "data/pk-clean.csv", row.names = FALSE)

# Cleaning workspace
rm(list = setdiff(ls(), "df"))